Controlled access areas have become increasingly commonplace in modern society. From hospitals to gated communities, sensitive industries to prison complexes, there is a need to control the flow of human capital. Systems for doing so are often rudimentary, such as those that utilize a guard who checks an individual's identification and access rights. Other systems involve physical keycards and passes, which allow access past static checkpoints.
These systems are often insufficient for controlling and tracking the movement of guests who have access for a particular, limited purpose. Once past a static checkpoint, control systems have limited means for tracking a guest's movements. Furthermore, guests may become lost or enter into areas beyond the scope of their invitation.
Furthermore, determining accurate indoor locations for people and objects has been the goal of numerous government, academia and corporate institutions for well over a decade. GPS, while excellent for outdoors location, isn't well suited for indoor location due to signal attenuation caused by the building materials causing significant power loss for the signals. Numerous technical approaches to accomplishing the goal of accurate indoor location have been researched, developed and tested though the effectiveness and accuracy of each of these methods can vastly differ.
The industry classifies indoor location technologies into infrastructure-based and infrastructure-free technologies. Infrastructure-based technologies require the installation and configuration of physical beacons, typically based on a radio-frequency technology such as IR, WiFi, RFID and Bluetooth but can also include beacons based on sound, magnetic signals or light. Infrastructure-free technologies, typically utilize the existing infrastructure available in a location such as WiFi access points, cellular/GSM signals, geo-magnetic and sound sources though they usually involve quite a bit of configuration in the form of fingerprinting, or the analysis of the specific properties of WiFi, Magnetic, Sound and other signals at various points within the room.
Once the initial infrastructure, fingerprinting, analysis and other implementation steps are done, the various systems currently in existence rely on various forms of signal triangulation, signal measurement, signal disturbance, movement detection, barometric pressure detection, or other forms to detect the location of a person in an indoor space. The technical names for these existing technologies or methods include, but are not limited to, 2.4 Ghz Phase Offset, 2.4 Ghz Time-of-Flight, Ultrasonic Time-of-Flight, IR/Radio Time-of-flight, Modulated Magnetic signals, WiFi+Bluetooth+IMU, WiFi Fingerprinting with Bayesian Filter, WiFi Fingerprinting with Neural Network, WiFi Time-of-Flight with Adaptive filter, WiFi+IMU Fingerprinting and Steerable Antenna Time-of-Flight.
The existing indoor location technologies have performance and accuracy issues related to a variety of factors including but not limited to requiring a line of sight between a persons' device and the beacons/sensors, signal bleed complications, interference from other sources, objects and construction materials, and even the human body acting as a barrier to a signal. Additionally, the overhead in terms of deployment of custom infrastructure, space evaluation, fingerprint analysis, system configuration, equipment costs and other system implementation related issues is relatively high for existing methodologies.
The current disclosure is directed at addressing or reducing the above issues, including, without limitation, with indoor location accuracy, performance and overhead with the additional benefit of user identification and tracking throughout a venue.